{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://mp.weixin.qq.com/s?__biz=MzkyMDMxNDkxOA==&mid=2247484535&idx=1&sn=68dda70a715766147b85c660f90ffd72&chksm=c195f13ef6e27828379d9fc3cdff8bcfd350fea22ec72cfe0fddfc950190dbc4da364a8a2586&scene=178&cur_album_id=3447886516519747585#rd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 振动升降指标ASI策略.py\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_hist_k_data(code,start_date,end_date,frequency='d')->pd.DataFrame:\n",
    "    \"\"\"\n",
    "    获取历史K线数据\n",
    "    :return:\n",
    "    \"\"\"\n",
    "    import baostock as bs\n",
    "    bs.login()\n",
    "    rs = bs.query_history_k_data_plus(code,\"date,code,open,high,low,close,preclose,volume,amount,pctChg\",start_date,end_date,frequency=frequency)\n",
    "    data_list = []\n",
    "    while (rs.error_code == '0') & rs.next():\n",
    "        # 获取一条记录，将记录合并在一起\n",
    "        data_list.append(rs.get_row_data())\n",
    "    result = pd.DataFrame(data_list, columns=rs.fields)\n",
    "    result.open = result.open.astype(float)\n",
    "    result.high = result.high.astype(float)\n",
    "    result.low = result.low.astype(float)\n",
    "    result.close = result.close.astype(float)\n",
    "    result.date= pd.to_datetime(result.date)\n",
    "    result.set_index('date',inplace=True)\n",
    "    bs.logout()\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "login success!\n",
      "logout success!\n"
     ]
    }
   ],
   "source": [
    "# 获取股票数据\n",
    "stock_symbol = 'sh.600030'\n",
    "start_date = '2005-01-01'\n",
    "end_date = '2024-07-22'\n",
    "data = get_hist_k_data(stock_symbol, start_date, end_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/xj/rt03m8y91wjcylw3wlbcvhzm0000gn/T/ipykernel_51350/438850816.py:12: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  df['signal'][roc_threshold:] = np.where(df['roc'][roc_threshold:] > roc_threshold, 1, 0)\n",
      "/var/folders/xj/rt03m8y91wjcylw3wlbcvhzm0000gn/T/ipykernel_51350/438850816.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['signal'][roc_threshold:] = np.where(df['roc'][roc_threshold:] > roc_threshold, 1, 0)\n"
     ]
    }
   ],
   "source": [
    "# 计算ROC指标\n",
    "def calculate_roc(df, period=12):\n",
    "    df['roc'] = df['close'].pct_change(period) * 100\n",
    "    return df\n",
    "\n",
    "# 生成交易信号\n",
    "data = calculate_roc(data)\n",
    "\n",
    "def trading_strategy(df, roc_threshold=10):\n",
    "    # 生成交易信号\n",
    "    df['signal'] = 0\n",
    "    df['signal'][roc_threshold:] = np.where(df['roc'][roc_threshold:] > roc_threshold, 1, 0)\n",
    "    df['Position'] = df['signal'].diff()\n",
    "    return df\n",
    "    \n",
    "data = trading_strategy(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 回测策略\n",
    "def backtest_strategy(data):\n",
    "    data['Strategy_Returns'] = data['Position'].shift(1) * data['close'].pct_change()\n",
    "    data['Cumulative_Returns'] = (1 + data['Strategy_Returns']).cumprod()\n",
    "    return data\n",
    "\n",
    "data = backtest_strategy(data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制策略的累计收益\n",
    "plt.figure(figsize=(14, 7))\n",
    "plt.plot(data['Cumulative_Returns'], label='AO Strategy')\n",
    "plt.title('AO Strategy Cumulative Returns')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Cumulative Returns')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tf",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
